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Results 1 - 9 of 9 for getAdjX (0.2 sec)
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tensorflow/compiler/mlir/lite/transforms/optimize_batch_matmul.cc
Value input_rhs = bmm_op.getY(); Value output_lhs = bmm_op.getAdjX() ? create_z_x_transpose_op(input_lhs) : input_lhs; // The rhs need to be transposed if adj_y == false AND this matmul will be // legalized to tfl.fully_connected Value output_rhs = !bmm_op.getAdjY() ? create_z_x_transpose_op(input_rhs) : input_rhs; Type output_type = bmm_op.getResult().getType();
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Thu Apr 25 16:01:03 UTC 2024 - 9.6K bytes - Viewed (0) -
tensorflow/compiler/mlir/tensorflow/transforms/unroll_batch_matmul.cc
return failure(); } // Replace the last 2 dimensions of LHS and RHS if necessary. // The actual transpose is done by MatMulOp. if (op.getAdjX()) { std::swap(lhs_shape[lhs_dims - 1], lhs_shape[lhs_dims - 2]); } if (op.getAdjY()) { std::swap(rhs_shape[rhs_dims - 1], rhs_shape[rhs_dims - 2]); } const int64_t rows = lhs_shape[lhs_dims - 2];
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Thu Apr 25 16:01:03 UTC 2024 - 11.6K bytes - Viewed (0) -
tensorflow/compiler/mlir/tensorflow/transforms/fold_broadcast.cc
} const int x_row = matmul_op.getAdjX() ? shape_x.back() : *(shape_x.rbegin() + 1); const int x_col = !matmul_op.getAdjX() ? shape_x.back() : *(shape_x.rbegin() + 1); const int y_row = matmul_op.getAdjY() ? shape_y.back() : *(shape_y.rbegin() + 1); const int y_col = !matmul_op.getAdjY() ? shape_y.back() : *(shape_y.rbegin() + 1);
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Thu Apr 25 16:01:03 UTC 2024 - 7.9K bytes - Viewed (0) -
tensorflow/compiler/mlir/tensorflow/transforms/batchmatmul_to_einsum.cc
if (dims_a < 2 || dims_b < 2) { return failure(); } // einsum equation for batchmatmul std::string equation("...mk,...kn->...mn"); if (op.getAdjX()) std::swap(equation[3], equation[4]); if (op.getAdjY()) std::swap(equation[6 + 3], equation[6 + 4]); rewriter.replaceOpWithNewOp<TF::EinsumOp>( op, op.getType(), /*inputs=*/ValueRange({input_lhs, input_rhs}),
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Thu Apr 25 16:01:03 UTC 2024 - 3.8K bytes - Viewed (0) -
tensorflow/compiler/mlir/lite/transforms/legalize_tf.cc
Value input_rhs = get_real_input_value(op.getY()); Value legalized_lhs = op.getAdjX() ? create_z_x_transpose_op(input_lhs) : input_lhs; // The rhs need to be transposed if adj_y == false AND this matmul will be // legalized to tfl.fully_connected Value legalized_rhs = !op.getAdjY() ? create_z_x_transpose_op(input_rhs) : input_rhs; Type output_type = op.getResult().getType();
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Mon May 20 20:06:54 UTC 2024 - 45.2K bytes - Viewed (0) -
tensorflow/compiler/mlir/lite/transforms/optimize.cc
auto new_reshape = rewriter.create<ReshapeOp>( op.getLoc(), transpose_op.getInput(), shape_constant); rewriter.replaceOpWithNewOp<BatchMatMulOp>( op, op.getType(), op.getX(), new_reshape, op.getAdjX(), !op.getAdjY(), op.getAsymmetricQuantizeInputsAttr()); return success(); } private: // Checks that tensor `a` has shape of [M, N, ...] and `b` has [M * N, ...].
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Tue Apr 30 00:40:15 UTC 2024 - 102.3K bytes - Viewed (0) -
tensorflow/compiler/mlir/tf2xla/transforms/legalize_tf.cc
if (!lhs_type || !rhs_type) return failure(); if (mlir::isa<ComplexType>(lhs_type.getElementType()) && op.getAdjX()) { lhs = rewriter.create<TF::ConjOp>(op.getLoc(), lhs_type, lhs); } if (mlir::isa<ComplexType>(rhs_type.getElementType()) && op.getAdjY()) { rhs = rewriter.create<TF::ConjOp>(op.getLoc(), rhs_type, rhs); } // Broadcast both operands.
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Tue Jun 11 20:00:43 UTC 2024 - 291.8K bytes - Viewed (0) -
tensorflow/compiler/mlir/tensorflow/ir/tf_ops_a_m.cc
int64_t out_row_dim = output_shape[output_shape.size() - 2]; int64_t out_col_dim = output_shape[output_shape.size() - 1]; int64_t expected_out_row_dim = op.getAdjX() ? x_col_dim : x_row_dim; int64_t expected_out_col_dim = op.getAdjY() ? y_row_dim : y_col_dim; if (expected_out_row_dim != ShapedType::kDynamic && out_row_dim != ShapedType::kDynamic && out_row_dim != expected_out_row_dim)
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Thu Apr 25 16:01:03 UTC 2024 - 146.7K bytes - Viewed (0) -
tensorflow/compiler/mlir/lite/ir/tfl_ops.cc
int64_t out_row_dim = output_shape[output_shape.size() - 2]; int64_t out_col_dim = output_shape[output_shape.size() - 1]; int64_t expected_out_row_dim = op.getAdjX() ? x_col_dim : x_row_dim; int64_t expected_out_col_dim = op.getAdjY() ? y_row_dim : y_col_dim; if (expected_out_row_dim != ShapedType::kDynamic && out_row_dim != ShapedType::kDynamic && out_row_dim != expected_out_row_dim)
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Thu May 02 09:41:17 UTC 2024 - 169.2K bytes - Viewed (0)